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EIEIO / APPLICATION TRAINING

Regression Analysis with Excel

Course outline

1 day
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Duration

1 day

What You will Learn

  • Get familiar with essential concepts of Data Science, Artificial Intelligence, and Machine Learning
  • Practically understand how to map a business problem in your organization into an analytics solution
  • Explore the AI practical use cases and how it can boost business performance
  • Learn how to evaluate Analytics and AI solutions and the impact they could have on your business
  • Learn how to build the in-house data science team in your organization
  • Know what kind of training or skills do people need to build a successful data science team
  • Know the concepts and applications of some advanced topics in data science like deep learning, block chain, and further related topics.

Course Outline

  • 1. Introduction to Data Analysis
  • Data vs. Information vs. Actionable Insights
  • Exploratory Analytics: Concept, Usage, and Techniques
  • Predictive Analytics: Concept, Usage, and Techniques
  • Prescriptive Analytics: Concept, Usage, and Techniques
  • 2. Plan a Real Analytics Project for Your Business
  • Domain study and identifying the business problem
  • Identifying your analytics project objective using the S.M.A.R.T method.
  • What data you have or need to prepare?
  • Analytics project planning
  • Mapping your business problem into analytics solution Gathering Business Knowledge
  • Data Exploration
  • The Data and the Data Dictionary
  • Univariate analysis and EDD
  • Descriptive Data Analytics in Excel
  • Outlier Treatment
  • Identifying and Treating Outliers in Excel
  • Missing Value Imputation
  • Identifying and Treating missing values in Excel
  • Variable Transformation in Excel
  • Dummy variable creation: Handling qualitative data
  • 3. Getting Data Ready for Regression Model
  • Dummy Variable Creation in Excel
  • Correlation Analysis
  • Creating Correlation Matrix in Excel
  • The Problem Statement
  • Basic Equations and Ordinary Least Squares (OLS) method
  • Assessing accuracy of predicted coefficients
  • Assessing Model Accuracy: RSE and R squared
  • Creating Simple Linear Regression model
  • Multiple Linear Regression
  • The F - statistic
  • Interpreting results of Categorical variables
  • Creating Multiple Linear Regression model
  • Excel: Running Linear Regression using Solver
  • 4. Creating Regression Model

Programme scope, delivery arrangements and suitability are confirmed during enquiry.